Zobrazeno 1 - 10
of 139
pro vyhledávání: '"Maspero, Matteo"'
Manual segmentation of medical images is labor intensive and especially challenging for images with poor contrast or resolution. The presence of disease exacerbates this further, increasing the need for an automated solution. To this extent, SynthSeg
Externí odkaz:
http://arxiv.org/abs/2406.17423
Autor:
Huijben, Evi M. C., Terpstra, Maarten L., Galapon, Arthur Jr., Pai, Suraj, Thummerer, Adrian, Koopmans, Peter, Afonso, Manya, van Eijnatten, Maureen, Gurney-Champion, Oliver, Chen, Zeli, Zhang, Yiwen, Zheng, Kaiyi, Li, Chuanpu, Pang, Haowen, Ye, Chuyang, Wang, Runqi, Song, Tao, Fan, Fuxin, Qiu, Jingna, Huang, Yixing, Ha, Juhyung, Park, Jong Sung, Alain-Beaudoin, Alexandra, Bériault, Silvain, Yu, Pengxin, Guo, Hongbin, Huang, Zhanyao, Li, Gengwan, Zhang, Xueru, Fan, Yubo, Liu, Han, Xin, Bowen, Nicolson, Aaron, Zhong, Lujia, Deng, Zhiwei, Müller-Franzes, Gustav, Khader, Firas, Li, Xia, Zhang, Ye, Hémon, Cédric, Boussot, Valentin, Zhang, Zhihao, Wang, Long, Bai, Lu, Wang, Shaobin, Mus, Derk, Kooiman, Bram, Sargeant, Chelsea A. H., Henderson, Edward G. A., Kondo, Satoshi, Kasai, Satoshi, Karimzadeh, Reza, Ibragimov, Bulat, Helfer, Thomas, Dafflon, Jessica, Chen, Zijie, Wang, Enpei, Perko, Zoltan, Maspero, Matteo
Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of radiation to tumors while sparing healthy tissues over multiple days. Computed tomography (CT) is integral for treatment planning, offering electron density
Externí odkaz:
http://arxiv.org/abs/2403.08447
Autor:
Jacobs, Luuk, Mandija, Stefano, Liu, Hongyan, Berg, Cornelis A. T. van den, Sbrizzi, Alessandro, Maspero, Matteo
Publikováno v:
Med Phys. (2023)
In this study, we develop a physics-informed deep learning-based method to synthesize multiple brain magnetic resonance imaging (MRI) contrasts from a single five-minute acquisition and investigate its ability to generalize to arbitrary contrasts to
Externí odkaz:
http://arxiv.org/abs/2305.12570
Autor:
Thummerer, Adrian, van der Bijl, Erik, Galapon, Arthur Jr, Verhoeff, Joost JC, Langendijk, Johannes A, Both, Stefan, Cornelis, Berg, AT van den, Maspero, Matteo
Purpose: Medical imaging has become increasingly important in diagnosing and treating oncological patients, particularly in radiotherapy. Recent advances in synthetic computed tomography (sCT) generation have increased interest in public challenges t
Externí odkaz:
http://arxiv.org/abs/2303.16320
Background: Synthetic computed tomography (sCT) has been proposed and increasingly clinically adopted to enable magnetic resonance imaging (MRI)-based radiotherapy. Deep learning (DL) has recently demonstrated the ability to generate accurate sCT fro
Externí odkaz:
http://arxiv.org/abs/2303.10202
Publikováno v:
Med Phys. 2023; 1-12
Background: Respiratory-resolved four-dimensional magnetic resonance imaging (4D-MRI) provides essential motion information for accurate radiation treatments of mobile tumors. However, obtaining high-quality 4D-MRI suffers from long acquisition and r
Externí odkaz:
http://arxiv.org/abs/2211.05678
Autor:
Maspero, Matteo <1976>
Two Asian longhorned beetles (Coleoptera: Cerambycidae), commonly known as Citrus Longhorned Beetle (CLB), Anoplophora chinensis (Forster), and Asian Longhorned Beetle (ALB), A. glabripennis (Motschulsky), are considered the most destructive wood bor
Externí odkaz:
http://amsdottorato.unibo.it/7184/
Autor:
Reinders, Floris C.J., Savenije, Mark H.F., de Ridder, Mischa, Maspero, Matteo, Doornaert, Patricia A.H., Terhaard, Chris H.J., Raaijmakers, Cornelis P.J., Zakeri, Kaveh, Lee, Nancy Y., Aliotta, Eric, Rangnekar, Aneesh, Veeraraghavan, Harini, Philippens, Marielle E.P.
Publikováno v:
In Physics and Imaging in Radiation Oncology October 2024 32
Autor:
Huijben, Evi M.C., Terpstra, Maarten L., Galapon, Arthur Jr., Pai, Suraj, Thummerer, Adrian, Koopmans, Peter, Afonso, Manya, van Eijnatten, Maureen, Gurney-Champion, Oliver, Chen, Zeli, Zhang, Yiwen, Zheng, Kaiyi, Li, Chuanpu, Pang, Haowen, Ye, Chuyang, Wang, Runqi, Song, Tao, Fan, Fuxin, Qiu, Jingna, Huang, Yixing, Ha, Juhyung, Sung Park, Jong, Alain-Beaudoin, Alexandra, Bériault, Silvain, Yu, Pengxin, Guo, Hongbin, Huang, Zhanyao, Li, Gengwan, Zhang, Xueru, Fan, Yubo, Liu, Han, Xin, Bowen, Nicolson, Aaron, Zhong, Lujia, Deng, Zhiwei, Müller-Franzes, Gustav, Khader, Firas, Li, Xia, Zhang, Ye, Hémon, Cédric, Boussot, Valentin, Zhang, Zhihao, Wang, Long, Bai, Lu, Wang, Shaobin, Mus, Derk, Kooiman, Bram, Sargeant, Chelsea A.H., Henderson, Edward G.A., Kondo, Satoshi, Kasai, Satoshi, Karimzadeh, Reza, Ibragimov, Bulat, Helfer, Thomas, Dafflon, Jessica, Chen, Zijie, Wang, Enpei, Perko, Zoltan, Maspero, Matteo
Publikováno v:
In Medical Image Analysis October 2024 97
Recently, deep learning (DL)-based methods for the generation of synthetic computed tomography (sCT) have received significant research attention as an alternative to classical ones. We present here a systematic review of these methods by grouping th
Externí odkaz:
http://arxiv.org/abs/2102.02734